Enhance contest file loading and player data calculations

- Updated the load_contest_file function to return an additional dictionary for actual points, improving data accuracy.
- Modified app.py to incorporate actual points in player calculations, enhancing the analysis of player performance.
- Introduced new sets to track player usage based on actual points, improving data insights for user interaction.

app.py

@@ -33,7 +33,7 @@ with tab1:
             del st.session_state['Contest']
 
         if Contest_file:
-            st.session_state['Contest'], st.session_state['ownership_dict'], st.session_state['entry_list'] = load_contest_file(Contest_file, sport_select)
+            st.session_state['Contest'], st.session_state['ownership_dict'], st.session_state['actual_dict'], st.session_state['entry_list'] = load_contest_file(Contest_file, sport_select)
             st.session_state['Contest'] = st.session_state['Contest'].dropna(how='all')
             st.session_state['Contest'] = st.session_state['Contest'].reset_index(drop=True)
             if st.session_state['Contest'] is not None:
@@ -132,6 +132,7 @@ with tab2:
             )
             working_df['salary'] = working_df.apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1)
             working_df['median'] = working_df.apply(lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row), axis=1)
+            working_df['actual'] = working_df.apply(lambda row: sum(st.session_state['actual_dict'].get(player, 0) for player in row), axis=1)
             working_df['Own'] = working_df.apply(lambda row: sum(map_dict['own_map'].get(player, 0) for player in row), axis=1)
             working_df['sorted'] = working_df[player_columns].apply(
                 lambda row: ','.join(sorted(row.values)),
@@ -178,10 +179,18 @@ with tab2:
         
 
         contest_players = set()
+        players_1per = set()
+        players_5per = set()
+        players_10per = set()
+        players_20per = set()
         for col in player_columns:
             contest_players.update(working_df[col].unique())
+            players_1per.update(working_df.nlargest(n=int(len(working_df) * 0.01), columns='actual')[col].unique())
+            players_5per.update(working_df.nlargest(n=int(len(working_df) * 0.05), columns='actual')[col].unique())
+            players_10per.update(working_df.nlargest(n=int(len(working_df) * 0.10), columns='actual')[col].unique())
+            players_20per.update(working_df.nlargest(n=int(len(working_df) * 0.20), columns='actual')[col].unique())
         with st.container():
-            tab1, tab2 = st.tabs(['Player Used Info', 'Stack Used Info', 'Projection Info'])
+            tab1, tab2 = st.tabs(['Player Used Info', 'Stack Used Info'])
             with tab1:
                 player_counts = contest_players.value_counts()
                 player_frame = player_counts.to_frame().reset_index().rename(columns={'index': 'Player', 0: 'Count'})

global_func/load_contest_file.py

@@ -49,12 +49,13 @@ def load_contest_file(upload, sport):
             if sport == 'MLB':
                 df = df.rename(columns={1: '1B', 2: '2B', 3: '3B', 4: 'C', 5: 'OF1', 6: 'OF2', 7: 'OF3', 8: 'SP1', 9: 'SP2', 10: 'SS'})
             ownership_dict = dict(zip(df['Player'], df['Own']))
+            fpts_dict = dict(zip(df['Player'], df['FPTS']))
             cleaned_df = df.drop(columns=['EntryId', 'EntryName', 'TimeRemaining', 'Points', 'Lineup', 'Player', 'Pos', 'Own', 'FPTS'])
             cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']]
             entry_list = list(set(df['BaseName']))
             entry_list.sort()
             
-            return cleaned_df, ownership_dict, entry_list
+            return cleaned_df, ownership_dict, fpts_dict, entry_list
         except Exception as e:
             st.error(f'Error loading file: {str(e)}')
             return None